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<p>PatternNet: Visual Pattern Mining with Deep Neural Network<br />
作者单位为微软研究院 Microsoft Research, Redmond<br />
发表于 ACM ICMR 2018</p>
</blockquote>
<h2 id="abstract"><a class="markdownIt-Anchor" href="#abstract"></a> ABSTRACT</h2>
<ul>
<li>视觉模式(visual pattern)表了视觉世界中可以识别的规律性。它们捕捉了视觉对象或者场景中的最基本的性质(essential nature).理解视觉模式并对视觉模式进行建模是视觉识别中的一个基本问题。本论文提出了一个新的深度神经网络叫做PatternNet，用来发现既具有区别性又具有代表性的视觉模式</li>
<li>PatternNet利用最后一个卷积层的滤波器(fliter)来寻找局部一致的视觉块,并且与这些滤波器结合起来可以有效地发现独特的视觉模式</li>
<li>另：PatternNet可以在不经过图像patch采样(对patch进行采样的开销是巨大的)的情况下有效地发现视觉模式，因此这种方式的训练速度很快</li>
</ul>
<p><font color=gray>注：视觉模式-visual pattern  区别性-discriminative  代表性-representative 滤波器-filter，即CNN中的卷积核-Convolution kernel</font></p>
<h2 id="introduction"><a class="markdownIt-Anchor" href="#introduction"></a> INTRODUCTION</h2>
<p>读完摘要可能会有一些疑惑，在此将对摘要中的内容进行扩充说明：</p>
<h5 id="1-什么是visual-pattern如何定义visual-pattern在一个具体的神经网络中如何找到视觉模式"><a class="markdownIt-Anchor" href="#1-什么是visual-pattern如何定义visual-pattern在一个具体的神经网络中如何找到视觉模式"></a> 1. 什么是visual pattern？如何定义visual pattern？在一个具体的神经网络中如何找到视觉模式？</h5>
<p>视觉模式(visual pattern)是最基本的视觉元素，通常出现在图像中，但往往比原始像素传达更高层次的语义信息。例如基础一点的线、点、正方形圆形或者高级一点的轮子、门、车、马等都可以看做视觉模式。<br />
传统的计算机视觉方法如SIFT特征检测就被认为是低水平的视觉模式检测方法。CNN中的卷积层可以看作是特征提取或视觉模式挖掘的一种形式。CNN最近被证明具有非凡的视觉识别能力，其中一种主流的解释是经过训练的卷积层能够捕获图像集的<strong>局部纹理模式</strong>(local texture patterns)<br />
因此该论文作者设计一个<strong>反卷积神经网络</strong>(Deconvolution)来直观演示卷积神经网络中每个卷积滤波器所捕获的信息。对于一个滤波器，反卷积经由整个网络进行回溯并且找出原始图像中哪些像素对该滤波器的响应(response of the fliter 即卷积之后输出的图像)有贡献。使用反卷积神经网络，我们可以发现每一个滤波器都对某些visual appearance或pattern敏感并且能描述出每一个滤波器具体是对哪些appearance或者pattern敏感。<font color=red>实验说明：第一个或者前两个卷积层能够捕获简单的纹理，如线或角；而最后几层能够捕获具有语义的模式，并且这些模式在外观上有很大的差异。</font><br />
一个典型的CNN网络，例如AlexNet的最后一层conv5具有256个滤波器，与现实世界中可能出现的模式相比来说是一个很小的数字，这表明可能多个模式共享一个滤波器；当然同时还发现：如果一个图像patch里面含有多个模式(这些模式也与多个滤波器相关联)，因而也可以同时触发多个不同的滤波器。因而该论文提出了一个利用CNN中激活的滤波器来自动发现视觉模式的框架，即PatternNet。</p>
<h5 id="2如何定义区别性-代表性和视觉模式挖掘"><a class="markdownIt-Anchor" href="#2如何定义区别性-代表性和视觉模式挖掘"></a> 2.如何定义区别性、代表性和视觉模式挖掘？</h5>
<ul>
<li><strong>区别性</strong>(discriminative) 表示在一个类别中发现的模式应当与其他类别中发现的模式显著不同即，在同一类别的图像中出现的独特视觉元素</li>
<li><strong>代表性</strong>(representative) 要求模式应该频繁出现在出现在同一类别的图像中</li>
<li><strong>视觉模式挖掘</strong>(visual pattern mining) 给定一组类别的图片称为正类(positive images),和一组来自其他类别的图像称为负类(negative images)。为了找到具有<font color=red>代表性</font>的视觉模式,可以从区别正负类入手。而<font color=red>区别性</font>保证了我们找到的模式对这项任务是有用的。如果一种模式只出现在正类，而不出现在负类中，我们称之具有区别性。如果一个模式在正面图像中出现多次，我们称之具有代表性。</li>
</ul>
<h5 id="3patternnet是如何设计的又是如何发现视觉模式的"><a class="markdownIt-Anchor" href="#3patternnet是如何设计的又是如何发现视觉模式的"></a> 3.PatternNet是如何设计的？又是如何发现视觉模式的？</h5>
<p>依照卷积神经网络中的滤波器的一些特性（每一个滤波器对于一个确定的视觉模式都有一致的响应），本论文设计了一个特殊的全连接层和损失函数用以寻找<strong>图像中对目标类别具有强响应并且对其他类别具有弱响应的滤波器组合</strong>(a combination of filters which have strong response to the patterns in the images from the target category and weak response to the images from other categories)，具体的网络设计见VISUAL PATTERN MINING部分<br />
<img src="https://s2.ax1x.com/2020/01/27/1uS5RI.png" alt="PatternNet" title="PatternNet" /><br />
如上图PatternNet部分所示，该论文作者在最后一个卷积层和全连接层之间引入一个全局池化层(global pooling layer)，池化方法为max-pooling，实现了查找可视模式的移位不变特性（shift-invariant property）。这使我们能够在图像patch级别上找到视觉模式，而无需将图像预先采样为patch。</p>
<h2 id="visual-pattern-mining"><a class="markdownIt-Anchor" href="#visual-pattern-mining"></a> VISUAL PATTERN MINING</h2>
<p>卷积层的激活可以应用于目标分割问题，这是由于：</p>
<ol>
<li>滤波器通常对某些模式有选择性</li>
<li>滤波器是空间局部的，它的响应映射可以被用于定位在原始输入图像中有兴趣对象的图像patch</li>
</ol>
<p><img src="https://s2.ax1x.com/2020/01/27/1ukGtS.png" alt="最后一层卷积层的局部可视化" title="最后一层卷积层的局部可视化" /><br />
上图是在不同的图像上，在CNN的最后一个卷积层中，对**同一个滤波器（每一行为一个滤波器）**的局部响应区域进行可视化处理得到的结果。通过上图我们知道，同一个滤波器可以被不同的视觉模式激活，另一方面，一个视觉模式也可能激活多个过滤器。<br />
第二行展示了滤波器α的局部响应区域，第三行展示了滤波器β的局部响应区域，可以看出：{α,β}这一组合表示flight。所以本文的策略便是用一系列滤波器的组合表示某一视觉模式。<br />
PatternNet的结构如下图所示：<br />
<img src="https://s2.ax1x.com/2020/01/27/1ueLwR.png" alt="PatternNet结构" /></p>
<ul>
<li>我们在最后一个卷积层之后使用<strong>全局最大池化层</strong>来丢弃位置信息，这使得全局最大池后的特征更加紧凑，可以有效地总结来自任何位置的输入，从而使得模式挖掘算法更快、更健壮。</li>
<li>当我们需要对视觉模式进行定位时，我们利用反卷积神经网络(即上图中的DeconvolutionNet)来获取位置信息。</li>
<li>feature map经过max pooling后送入阈值层，阈值化操作使得我们可以判断哪些滤波器被激活(高于阈值记为1，不高于记为0)，使用一个全连接层来表示视觉模式的滤波器选择,再加入sigmoid函数表示该视觉模式在输入图像中出现的概率。</li>
</ul>
<p><strong>cost function：</strong><br />
<img src="https://s2.ax1x.com/2020/01/27/1uudoR.png" alt="cost" title="cost" /><br />
(markdown公式难写，就不往下写了)</p>
<p>PatternNet经过学习之后，我们可以通过网络参数发现视觉模式。例如全连接层有Nf*Nc维的权重，Nc是最后一个卷积层的滤波器数目，Nf是可调参数，它表示所发现模式的期望数量(想发现30个模式就设置为30)。通过查找PatternNet中FC层的权重可以找到Nf个滤波器组合。每个过滤器的组合都能够从给定的图像集中找到一个视觉模式。</p>
<h2 id="code"><a class="markdownIt-Anchor" href="#code"></a> CODE</h2>
<p>网上没有找到这篇论文的代码，可能是我的冲浪技术还不太行吧，但是发现ICLR 2018上有一篇文章也提出了PatternNet的构思，而且在此之上提出了PatternAttribution，文章名为：<br />
<a href="https://arxiv.org/abs/1705.05598" target="_blank" rel="noopener" title="*Learning how to explain neural networks: PatternNet and PatternAttribution*"><em>Learning how to explain neural networks: PatternNet and PatternAttribution</em></a><br />
两者研究方向应该差不多，所以放到这里，同时后者提供了代码：<a href="https://github.com/pikinder/nn-patterns" target="_blank" rel="noopener" title="github">github</a><br />
2019年该团队在期刊JMLR发表了一篇名为<a href="https://arxiv.org/abs/1808.04260" target="_blank" rel="noopener" title="iNNvestigate Neural Networks!">iNNvestigate Neural Networks!</a>的论文。该团队制作了一个神经网络分析库，提供了目前常见的解释神经网络的一些方法如：LRP,PatternNet,PatternAttribution等，<a href="https://github.com/albermax/innvestigate" target="_blank" rel="noopener" title="github代码见链接">github代码见链接</a></p>

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            <p>原文作者：<a href="http://xiuzhedorothy.gitee.io">宇航猫休蛰</a>
            <p>原文链接：<a href="http://xiuzhedorothy.gitee.io/2020/06/28/patternnet-visual-pattern-mining-with-deep-neural-network-lun-wen-bi-ji/">http://xiuzhedorothy.gitee.io/2020/06/28/patternnet-visual-pattern-mining-with-deep-neural-network-lun-wen-bi-ji/</a>
            <p>发表日期：<a href="http://xiuzhedorothy.gitee.io/2020/06/28/patternnet-visual-pattern-mining-with-deep-neural-network-lun-wen-bi-ji/">June 28th 2020, 9:53:24 am</a>
            <p>更新日期：<a href="http://xiuzhedorothy.gitee.io/2020/06/28/patternnet-visual-pattern-mining-with-deep-neural-network-lun-wen-bi-ji/">March 30th 2021, 3:22:35 pm</a>
            <p>版权声明：本文采用<a rel="license noopener" href="http://creativecommons.org/licenses/by-nc/4.0/" target="_blank">知识共享署名-非商业性使用 4.0 国际许可协议</a>进行许可</p>
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        <ol class="toc"><li class="toc-item toc-level-2"><a class="toc-link" href="#abstract"><span class="toc-number">1.</span> <span class="toc-text"> ABSTRACT</span></a></li><li class="toc-item toc-level-2"><a class="toc-link" href="#introduction"><span class="toc-number">2.</span> <span class="toc-text"> INTRODUCTION</span></a><ol class="toc-child"><li class="toc-item toc-level-5"><a class="toc-link" href="#1-什么是visual-pattern如何定义visual-pattern在一个具体的神经网络中如何找到视觉模式"><span class="toc-number">2.0.0.1.</span> <span class="toc-text"> 1. 什么是visual pattern？如何定义visual pattern？在一个具体的神经网络中如何找到视觉模式？</span></a></li><li class="toc-item toc-level-5"><a class="toc-link" href="#2如何定义区别性-代表性和视觉模式挖掘"><span class="toc-number">2.0.0.2.</span> <span class="toc-text"> 2.如何定义区别性、代表性和视觉模式挖掘？</span></a></li><li class="toc-item toc-level-5"><a class="toc-link" href="#3patternnet是如何设计的又是如何发现视觉模式的"><span class="toc-number">2.0.0.3.</span> <span class="toc-text"> 3.PatternNet是如何设计的？又是如何发现视觉模式的？</span></a></li></ol></li></ol></li></ol></li><li class="toc-item toc-level-2"><a class="toc-link" href="#visual-pattern-mining"><span class="toc-number">3.</span> <span class="toc-text"> VISUAL PATTERN MINING</span></a></li><li class="toc-item toc-level-2"><a class="toc-link" href="#code"><span class="toc-number">4.</span> <span class="toc-text"> CODE</span></a></li></ol>
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